@inproceedings{bar-haim-etal-2017-improving,
title = "Improving Claim Stance Classification with Lexical Knowledge Expansion and Context Utilization",
author = "Bar-Haim, Roy and
Edelstein, Lilach and
Jochim, Charles and
Slonim, Noam",
editor = "Habernal, Ivan and
Gurevych, Iryna and
Ashley, Kevin and
Cardie, Claire and
Green, Nancy and
Litman, Diane and
Petasis, Georgios and
Reed, Chris and
Slonim, Noam and
Walker, Vern",
booktitle = "Proceedings of the 4th Workshop on Argument Mining",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-5104",
doi = "10.18653/v1/W17-5104",
pages = "32--38",
abstract = "Stance classification is a core component in on-demand argument construction pipelines. Previous work on claim stance classification relied on background knowledge such as manually-composed sentiment lexicons. We show that both accuracy and coverage can be significantly improved through automatic expansion of the initial lexicon. We also developed a set of contextual features that further improves the state-of-the-art for this task.",
}
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%0 Conference Proceedings
%T Improving Claim Stance Classification with Lexical Knowledge Expansion and Context Utilization
%A Bar-Haim, Roy
%A Edelstein, Lilach
%A Jochim, Charles
%A Slonim, Noam
%Y Habernal, Ivan
%Y Gurevych, Iryna
%Y Ashley, Kevin
%Y Cardie, Claire
%Y Green, Nancy
%Y Litman, Diane
%Y Petasis, Georgios
%Y Reed, Chris
%Y Slonim, Noam
%Y Walker, Vern
%S Proceedings of the 4th Workshop on Argument Mining
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F bar-haim-etal-2017-improving
%X Stance classification is a core component in on-demand argument construction pipelines. Previous work on claim stance classification relied on background knowledge such as manually-composed sentiment lexicons. We show that both accuracy and coverage can be significantly improved through automatic expansion of the initial lexicon. We also developed a set of contextual features that further improves the state-of-the-art for this task.
%R 10.18653/v1/W17-5104
%U https://aclanthology.org/W17-5104
%U https://doi.org/10.18653/v1/W17-5104
%P 32-38
Markdown (Informal)
[Improving Claim Stance Classification with Lexical Knowledge Expansion and Context Utilization](https://aclanthology.org/W17-5104) (Bar-Haim et al., ArgMining 2017)
ACL